Lower and upper critical solution temperatures of binary polymeric solutions

Ejraei, Ayoub; Shirvani, Samira; Aroon, Mohammad Ali; Khansary, Milad Asgarpour; Khalaj, Sepideh

doi:10.1016/j.fluid.2016.06.036

Cited by 16 publications

(10 citation statements)

References 24 publications

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“…Our earliest understanding of polymer solution thermodynamics began with the lattice model of Flory and Huggins. − While Flory–Huggins theory can capture the phase behavior quantitatively for a few select polymer–solvent combinations, the earliest versions of this theory are oversimplified due to its underlying assumptions . Thus, additional lattice models and improvements have been derived based on the original Flory–Huggins model. − Other models followed, such as equation of state (EoS), quantity structure–property relationship (QSPR), , and activity coefficient models. , Many of these models require solving multiple complex equations simultaneously that are highly sensitive to the initial guess or require properties of the pure components that are either unknown or difficult to measure. In most cases, only qualitative agreement with experiments can be obtained, and each polymer–solvent system requires its own model parametrization.…”

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confidence: 99%

“…3 Thus, additional lattice models and improvements have been derived based on the original Flory− Huggins model. 7−17 Other models followed, such as equation of state (EoS), 18 quantity structure−property relationship (QSPR), 19,20 and activity coefficient models. 21,22 Many of these models require solving multiple complex equations simultaneously that are highly sensitive to the initial guess or require properties of the pure components that are either unknown or difficult to measure.…”

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Deep Learning of Binary Solution Phase Behavior of Polystyrene

et al. 2021

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Predicting binary solution phase behavior of polymers has remained a challenge since the early theory of Flory−Huggins, hindering the processing, synthesis, and design of polymeric materials. Herein, we take a complementary data-driven approach by building a machine learning framework to make fast and accurate predictions of polymer solution cloud point temperatures. Using polystyrene, both upper and lower critical solution temperatures are predicted within experimental uncertainty (1−2 °C) with a deep neural network, Gaussian process regression (GPR) model, and a combination of polymer, solvent, and state features. The GPR model also enables intelligent exploration of solution phase space, where as little as 25 cloud points are required to make predictions within 2 °C for polystyrene of arbitrary molecular weight in cyclohexane. This study demonstrates the effectiveness of machine learning for the prediction of liquid−liquid equilibrium of polymer solutions and establishes a framework to incorporate other polymers and complex macromolecular architectures.

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Deep Learning of Binary Solution Phase Behavior of Polystyrene

et al. 2021

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“…In Figure 4, the averaged mixing energies of water and cellulose variants (see section 2) are displayed. Generally, the energy of mixing should always be negative where mixing is possible and spontaneous [37]. From Figure 4, it can be concluded that each cellulose variant shows different tendencies for aggregation and agglomeration.…”

Section: Flory-huggins Theory Combined With Molecular Dynamicsmentioning

confidence: 96%

A molecular scale analysis of TEMPO-oxidation of native cellulose molecules

Khansary

Pouresmaeel-Selakjani

Aroon

et al. 2020

Heliyon

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The native cellulose, through TEMPO (2,2,6,6-tetramethylpiperidine-1-oxyl radical)-mediated oxidation, can be converted into individual fibers. It has been observed that oxidized fibers disperse completely and individually in water. It is believed that electrostatic repulsive forces might be responsible for such observations. In order to study the TEMPO-oxidation of cellulose molecules, we used Density Functional Theory (DFT) calculations and Flory-Huggins theory combined with molecular dynamics (MD). The surface electrostatic potential in native cellulose and TEMPO-oxidized cellulose were calculated using DFT calculations. We found that TEMPO-oxidized cellulose accommodates a threefold screw conformation where the negatively charged (-COO-) functional groups are pointed away from the surface in all spatial directions. This spatial orientation causes that TEMPO-oxidized cellulose molecules repulse each other due to strong negatively charged surface. At the same time, the spatial orientation increases the hydrophilicity in TEMPO-oxidized cellulose molecules. These observations explain the improved dispersion in water and separability of TEMPO-oxidized cellulose molecules. We obtained large and positive Flory-Huggins interaction parameters for TEMPO-oxidized cellulose molecules indicating their higher dispersion once in water.

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“…For the latter system, the solvent critical temperature was estimated using Kay’s rule . For pure solvents, other methods in more recent work include those based on solvent critical density and temperature, connectivity indices of the solvent and polymer, molecular descriptors and quantitative structure–activity/property relationships, artificial neural networks, and thermodynamic theories . These methods are, however, not only computationally involved, but also consider only pure solvents which are liquids at ambient conditions.…”

Section: Introductionmentioning

confidence: 99%

Role of Solvent Properties in the High-Pressure Phase Behavior of (Ethylene + Comonomer + n-Hexane + LLDPE) Systems

Swanepoel

Schwarz

2019

J. Chem. Eng. Data

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The lower critical end point temperatures of [ethylene + comonomer + n-hexane + linear low-density polyethylene (LLDPE)] systems are shown to be strongly correlated with solvent average molecular mass and mole fraction-averaged carbon number, but not solvent density or critical temperature. A synthetic-visual method is used to measure vapor–liquid, vapor–liquid–liquid, and liquid–liquid phase boundaries for quasiquaternary (ethylene + comonomer + n-hexane + LLDPE) systems for the 1-butene, 4-methyl-1-pentene, 1-hexene, 1-octene, and 1-decene comonomers, and for quasiternary (ethane/n-decane/4-methyl-1-pentene + n-hexane + LLDPE) systems. The reported data span temperatures of T = (375–465) K and pressures of P = (0.5–15) MPa. In all systems, the mass fraction of the LLDPE (M̅ w = 199 kg·mol–1; M̅ w/M̅ n = 2.62; 2.56 mol % 1-hexene) was kept at 3 wt % and solvent compositions were chosen to represent conditions found in the solution polymerization process. The modified Sanchez–Lacombe equation of state can predict the quasiquaternary systems’ phase behavior with moderate accuracy using parameters adjusted to quasiternary data.

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Lower and upper critical solution temperatures of binary polymeric solutions

Cited by 16 publications

References 24 publications

Deep Learning of Binary Solution Phase Behavior of Polystyrene

Deep Learning of Binary Solution Phase Behavior of Polystyrene

A molecular scale analysis of TEMPO-oxidation of native cellulose molecules

Role of Solvent Properties in the High-Pressure Phase Behavior of (Ethylene + Comonomer + n-Hexane + LLDPE) Systems

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